Machine learning is increasingly playing a larger and more important role in developing or improving the understanding of complex systems. As machine learning techniques have matured, machine learning has rapidly moved from the theoretical to the practical. Combined with the advent of big-data technology, machine learning solutions are being applied to a variety of industries and applications that until now were difficult, if not impossible to effectively reason about. As such, there has been an explosion of the development of different types of machine learning models that may be used predicting outcomes for different system. In some cases, organizations may develop many machine learning models that may be directed to different question spaces. Also, organizations may be interested in borrowing machine learning models, sharing machine learning models, cooperatively developing machine learning models, or the like. However, machine learning models may often be developed using custom handcrafted designs tailored for individual data sets or for a specific problems. Accordingly, practical re-use, sharing, or the like, of machine learning models may be difficult and impractical. Thus, it is with respect to these considerations and others that the invention has been made.